Panel can be used to create dashboards with the pythonic interface. This library explores the option to develop time-series dashboard using the panel.
panel_dashboard library takes in time series data as input and creates three plots in the dashboard for the selected tag.
1. Tag values vs timestamps
2. Histogram of the tag values
3. Description pane for the tag values
To understand the options available to create timeseries dashboard use the following command.
python3 panel_dashboard/dashboard.py --help
python3 timeseries.py --help
usage: timeseries.py [-h] [-f FILENAME]
[-ft {csv,excel,feather,hdf,parquet,pickle}]
[-kwgs [READ_KWARGS [READ_KWARGS ...]]]
[-fa FEATURE_ALIASES] [-d DESCRIBE] [-p PORT]
[-s SAMPLE_RATE] [-w WEBSOCKET_ORIGIN]
Create a dashboard with a pandas readable file (csv, hdf, parquet etc.)
optional arguments:
-h, --help show this help message and exit
-f FILENAME, --filename FILENAME
Location of file. By default, it accepts csv file that
should be readable as pd.read_parquet(filename). To
accept different type of file use the filetype and
read_kwargs arguments, (type: str) (default:
/mnt/data/final/historical_stock_prices.parquet)
-ft {csv,excel,feather,hdf,parquet,pickle}, --filetype {csv,excel,feather,hdf,parquet,pickle}
Type of file that is provided at filename argument.
Available options can be csv, excel, feather, hdf,
parquet, pickle etc., (type: str) (default: parquet)
-kwgs [READ_KWARGS [READ_KWARGS ...]], --read-kwargs [READ_KWARGS [READ_KWARGS ...]]
Additional keyword arguments to read the {filename}
using the pandas read_{filetype}. Keywords should be
provided in the following format -kwgs foo1=bar
foo2=10(type: str) (default: None)
-fa FEATURE_ALIASES, --feature-aliases FEATURE_ALIASES
Path to the json file which contains dictionary object
with keys as column names and values as column name
aliases, (type: str) (default:
/mnt/data/final/feature_aliases.json)
-d DESCRIBE, --describe DESCRIBE
Should the dashboard include
pandas.tag_series.describe() along with the plot,
(type: bool) (default: True)
-p PORT, --port PORT Port to launch the dashboard on, (type: int) (default:
5006)
-s SAMPLE_RATE, --sample_rate SAMPLE_RATE
Fraction of data that should be used for plots. A
float between (0.0, 1.0] that defaults to 1.0
(default: 1.0)
-w WEBSOCKET_ORIGIN, --websocket-origin WEBSOCKET_ORIGIN
Websocket origin to allow (useful for running
remotely), (type: int) (default: 49179)
By using default options, the dashboard script utilizes the data available in data/final to create a dashboard of historical stock prices. The dataset is downloaded from Kaggle and converted to parquet format for convenience. To understand how the conversion is done, go through the jupyter notebooks available in notebooks folder.
Preview of the dashboard is posted here.
You will need to have Docker and docker-compose installed on your system to run the code.
- For Mac: https://store.docker.com/editions/community/docker-ce-desktop-mac
- For Windows: https://store.docker.com/editions/community/docker-ce-desktop-windows
- For Linux: Go to this page and choose the appropriate install for your Linux distro: https://www.docker.com/community-edition
$ sudo curl -L https://github.com/docker/compose/releases/download/1.21.0/docker-compose-$(uname -s)-$(uname -m) -o /usr/local/bin/docker-compose
$ sudo chmod +x /usr/local/bin/docker-compose
Test the installation:
$ docker-compose --version
docker-compose version 1.21.0, build 1719ceb
The runtime for panel_dashboard is inside a Docker container. There is a make command to launch the appropriate containers. To launch the docker containers and begin working on a CPU, run from the root directory of the repository:
make dev-startThis builds images using the Dockerfile in docker/Dockerfile, and runs containers named after the project directory. To see the running containers, run
docker ps
You should see three containers running. For example, on my laptop this looks like the below. On your machine the container ids and the names of the images and running containers will be different, i.e. they will have your username rather that sdey. In addition, the local ports will be different as well. That is expected.
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
f168e19b8b67 panel_dashboard_mlflow "bash -c 'mlflow ui …" 4 days ago Up 3 days 127.0.0.1:32770->5000/tcp panel_dashboard_mlflow_<username>
87f03baf686e panel_dashboard_bash "/bin/bash" 4 days ago Up 4 days 127.0.0.1:32768->8501/tcp panel_dashboard_bash_<username>
d9bd01600486 panel_dashboard_jupyter "bash -c 'cd /mnt &&…" 4 days ago Up 3 days 127.0.0.1:32769->8888/tcp panel_dashboard_jupyter_<username>To stop the containers associated with the project:
make dev-stop├── README.md <- Project README
├── configs
| └──config.yml <- Project configuration for python scripts
├── data <- Data cache folder
│ ├── final <- Data that will be used for reports and analysis
│ └── raw <- The original, immutable data dump.
├── docker
│ ├── Dockerfile <- New project Dockerfile that sources from base ML dev image
│ ├── docker-compose.yml <- Docker Compose configuration file
│ └── requirements.txt <- The requirements file for reproducing the analysis environment.
│ New libraries should be added in the requirements
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
├── pyproject.toml <- Config file used by black
├── tox.ini <- tox config file with settings for flake
├── Makefile <- Makefile for starting and stopping containers, lint, and local CI.